Loading...
Loading...
Please wait
For a long time, restaurants have optimized for one thing: traditional search.
“Best pizza near me.” “BBQ in Austin.” “Happy hour downtown.”
The rules were clear, the rankings predictable, and the strategy—local SEO, review management, mobile-friendly websites—was largely settled.
That era is over.
Avi Goren, co-founder and CEO of Marqii, says a fundamental shift is underway, one that will reorganize how guests discover restaurants and how restaurants are expected to show up online.
“AI search isn’t about links. It’s about answers,” Goren explains. “The guest used to search for one thing. Now they’re asking for an itinerary.”
Instead of picking a single restaurant, consumers ask AI models like ChatGPT, Claude, and Gemini to plan multi-day trips, filter by dietary restrictions, eliminate chains, evaluate hours, and curate options based on ratings and atmosphere.
And those models are surfacing restaurants using data patterns that look very different from the SEO playbook operators have relied on for 20 years.
Restaurant operators are entering a new battleground: the race to be discoverable in AI-generated results.
Below is a breakdown of what’s changed, what drives AI rankings today, and the specific steps operators must take to avoid being left behind.
-
The Search Playbook
* Eliminate PDF menus
* Fix your hours everywhere
* Make your website readable by machines
* Post with intent
* Respond to every review
-
The Rise of “Answer-Based” Search
When someone types “Best tacos in Denver” into Google, they expect a list of links, maps, and review sites.
But when that same person opens ChatGPT, the prompt looks totally different:
“I’m heading to Denver for a weekend with my kids. Give me a two-day itinerary with lunch and dinner options, only local restaurants with at least four stars, mostly within 10 minutes of the zoo.”
Google would never deliver that. AI search does it instantly.
Goren says this shift isn’t hypothetical—it’s already happening.
“This is consumer behavior,” he says. “And as an industry, we always have to go where the guest goes.”
The Data That AI Models Actually Rely On
Marqii analyzed how AI models assemble restaurant recommendations today. According to Goren, three sources dominate:
1. Third-Party Listings
Google Business Profiles, Yelp, TripAdvisor, DoorDash, Uber Eats—these citations remain the single largest input into AI restaurant recommendations.
“When your listings are wrong, stale, or incomplete,” Goren says, “you disappear.”
2. Your Own Website
Your website matters more than ever, but only the parts AI can actually read.
PDF menus? They’re dead weight.
AI models rely on structured, crawler-friendly data. If your menu is a flat file, an image, or a non-schema page, you’re invisible.
Hours matter too. Not just restaurant hours, but bar hours, pickup hours, delivery hours, brunch hours. AI wants granular accuracy.
3. Reviews
Reviews carry outsize influence in AI search.
But it’s no longer just your score. It’s also: How often you respond, how detailed your responses are, or how up-to-date the sentiment is.
“AI is reading your reviews faster than any human ever could,” Goren notes.“Your review strategy is now part of your discovery strategy.”
Photos, Captions, and Social Posts Are Search Inputs
Another major shift: AI models scrape far more sources than Google’s local SEO.
That means your Instagram photo captions matter a lot more now.
“If you post a picture of your Nashville hot chicken sandwich,” Goren says, “you need a descriptive caption. AI will pull from that.”
It’s not about hashtags—it’s about clarity. What’s the dish? What are the ingredients? What dietary category does it fit? What neig